Review of Available Products of Leaf Area Index and Their Suitability over the Formerly Soviet Central Asia

Leaf area index (LAI) is a key biophysical variable for environmental process modelling. Remotely sensed data have become the primary source for estimation of LAI at the scales from local to global. A summary of existing LAI data sets and a discussion of their appropriateness for the formerly Soviet Central Asia, especially Kazakhstan, which is known for its huge grassland area (about 2 million km2), are valuable for environmental modelling in this region. The paper gives a brief review of existing global LAI products, such as AVHRR LAI, MODIS LAI, and SPOT-VEGETATION LAI, and shows that validation of these products in Kazakhstan as well as in other countries of the formerly Soviet Central Asia has not been carried out yet. Apart from the global LAI products, there are just a few data sets retrieved by remote sensing methods at subregional and regional scales in Kazakhstan. More research activities are needed to focus on the validation of the available global LAI products over the formerly Soviet Central Asia and developing new LAI data sets suitable for application in environmental modelling at different scales in this region.

[1]  Mark A. Friedl,et al.  Scaling and uncertainty in the relationship between the NDVI and land surface biophysical variables: An analysis using a scene simulation model and data from FIFE , 1995 .

[2]  Pavel Propastin,et al.  Spatial non-stationarity and scale-dependency of prediction accuracy in the remote estimation of LAI over a tropical rainforest in Sulawesi, Indonesia. , 2009 .

[3]  William A. Niering,et al.  Vegetation of the Santa Catalina Mountains , 1963 .

[4]  Rattan Lal,et al.  Carbon sequestration in soils of central Asia , 2004 .

[5]  J. Chen,et al.  Retrieving Leaf Area Index of Boreal Conifer Forests Using Landsat TM Images , 1996 .

[6]  Jennifer L. Dungan,et al.  Comparison of regression and geostatistical methods for mapping Leaf Area Index (LAI) with Landsat ETM+ data over a boreal forest. , 2005 .

[7]  D. Randall,et al.  A Revised Land Surface Parameterization (SiB2) for Atmospheric GCMS. Part I: Model Formulation , 1996 .

[8]  Geoffrey M. Henebry.  Global change: Carbon in idle croplands , 2009, Nature.

[9]  Martin Kappas,et al.  Mapping Leaf Area Index over semi-desert and steppe biomes in Kazakhstan using satellite imagery and ground measurements. , 2009 .

[10]  Robert E. Wolfe,et al.  Vegetation Phenology Metrics Derived from Temporally Smoothed and Gap-Filled MODIS Data , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.

[11]  W. Cohen,et al.  An improved strategy for regression of biophysical variables and Landsat ETM+ data. , 2003 .

[12]  F. M. Danson,et al.  Extraction of vegetation biophysical parameters by inversion of the PROSPECT + SAIL models on sugar beet canopy reflectance data. Application to TM and AVIRIS sensors , 1995 .

[13]  Martin Kappas,et al.  Modeling Net Ecosystem Exchange for Grassland in Central Kazakhstan by Combining Remote Sensing and Field Data , 2009, Remote. Sens..

[14]  S. Running,et al.  Global Terrestrial Gross and Net Primary Productivity from the Earth Observing System , 2000 .

[15]  Martin Kappas,et al.  Modified light use efficiency model for assessment of carbon sequestration in grasslands of Kazakhstan: combining ground biomass data and remote-sensing , 2012 .

[16]  Ranga B. Myneni,et al.  Estimation of global leaf area index and absorbed par using radiative transfer models , 1997, IEEE Trans. Geosci. Remote. Sens..

[17]  Michael Sprintsin,et al.  The effect of spatial resolution on the accuracy of leaf area index estimation for a forest planted in the desert transition zone , 2007 .

[18]  D. Diner,et al.  Estimation of vegetation canopy leaf area index and fraction of absorbed photosynthetically active radiation from atmosphere‐corrected MISR data , 1998 .

[19]  F. Baret,et al.  Neural network estimation of LAI, fAPAR, fCover and LAI×Cab, from top of canopy MERIS reflectance data : Principles and validation , 2006 .

[20]  Martin Kappas,et al.  Retrieval of Coarse-Resolution Leaf Area Index over the Republic of Kazakhstan Using NOAA AVHRR Satellite Data and Ground Measurements , 2012, Remote. Sens..

[21]  R. Lacaze,et al.  Global mapping of vegetation parameters from POLDER multiangular measurements for studies of surface-atmosphere interactions: A pragmatic method and its validation , 2002 .

[22]  S. Running,et al.  Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data , 2002 .

[23]  R. Fensholt,et al.  Evaluation of MODIS LAI, fAPAR and the relation between fAPAR and NDVI in a semi-arid environment using in situ measurements , 2004 .

[24]  W. Verhoef.  Light scattering by leaf layers with application to canopy reflectance modeling: The Scattering by Arbitrarily Inclined Leaves (SAIL) model , 1984 .

[25]  Jing M. Chen,et al.  Locally adjusted cubic-spline capping for reconstructing seasonal trajectories of a satellite-derived surface parameter , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[26]  Jing M. Chen,et al.  Daily canopy photosynthesis model through temporal and spatial scaling for remote sensing applications , 1999 .

[27]  Martin Kappas,et al.  Inter-Annual Changes in Vegetation Activities and Their Relationship to Temperature and Precipitation in Central Asia from 1982 to 2003 , 2008 .

[28]  S. T. Gower,et al.  Worldwide Historical Estimates of Leaf Area Index, 1932-2000 , 2001 .

[29]  Y. Knyazikhin,et al.  Radiative transfer based scaling of LAI retrievals from reflectance data of different resolutions , 2003 .

[30]  Martin Kappas,et al.  Integration of Landsat ETM+ Data with Field Measurements for Mapping Leaf Area Index in the Grasslands of Central Kazakhstan , 2009 .

[31]  F. Baret,et al.  LAI and fAPAR CYCLOPES global products derived from VEGETATION. Part 2: validation and comparison with MODIS collection 4 products , 2007 .

[32]  Rasmus Fensholt,et al.  MODIS leaf area index products: from validation to algorithm improvement , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[33]  J. Amthor.  Terrestrial higher plant respiration and net primary production , 2001 .

[34]  J. Hicke,et al.  Global synthesis of leaf area index observations: implications for ecological and remote sensing studies , 2003 .

[35]  Frédéric Baret,et al.  Review of methods for in situ leaf area index determination Part I. Theories, sensors and hemispherical photography , 2004 .

[36]  Y. Knyazikhin,et al.  Validation and intercomparison of global Leaf Area Index products derived from remote sensing data , 2008 .

[37]  F. Baret,et al.  Review of methods for in situ leaf area index (LAI) determination: Part II. Estimation of LAI, errors and sampling , 2004 .

[38]  P. Bicheron.  A Method of Biophysical Parameter Retrieval at Global Scale by Inversion of a Vegetation Reflectance Model , 1999 .

[39]  S. Ustin,et al.  LEAF OPTICAL PROPERTIES: A STATE OF THE ART , 2000 .

[40]  Robert E. Wolfe,et al.  An Algorithm to Produce Temporally and Spatially Continuous MODIS-LAI Time Series , 2008, IEEE Geoscience and Remote Sensing Letters.

[41]  Stefan Erasmi,et al.  A physically based approach to model LAI from MODIS 250 m data in a tropical region , 2010, Int. J. Appl. Earth Obs. Geoinformation.

[42]  J. Privette,et al.  Impact of Tissue, Canopy, and Landscape Factors on the Hyperspectral Reflectance Variability of Arid Ecosystems , 2000 .

[43]  J. Privette,et al.  Early spatial and temporal validation of MODIS LAI product in the Southern Africa Kalahari , 2002 .

[44]  Shunlin Liang,et al.  Integrating MODIS and CYCLOPES Leaf Area Index Products Using Empirical Orthogonal Functions , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[45]  Ramakrishna R. Nemani,et al.  Measurement and remote sensing of LAI in Rocky Mountain montane ecosystems , 1997 .

[46]  C. Justice,et al.  A Revised Land Surface Parameterization (SiB2) for Atmospheric GCMS. Part II: The Generation of Global Fields of Terrestrial Biophysical Parameters from Satellite Data , 1996 .

[47]  F. Baret,et al.  Evaluation of Canopy Biophysical Variable Retrieval Performances from the Accumulation of Large Swath Satellite Data , 1999 .

[48]  J. Pisek,et al.  Comparison and validation of MODIS and VEGETATION global LAI products over four BigFoot sites in North America , 2007 .

[49]  Tim R. McVicar,et al.  Assessment of the MODIS LAI product for Australian ecosystems , 2006 .

[50]  Jan Pisek,et al.  Algorithm for global leaf area index retrieval using satellite imagery , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[51]  S. Leblanc,et al.  A Shortwave Infrared Modification to the Simple Ratio for LAI Retrieval in Boreal Forests: An Image and Model Analysis , 2000 .

[52]  R. Dickinson,et al.  Evaluation of the Utility of Satellite-Based Vegetation Leaf Area Index Data for Climate Simulations , 2001 .

[53]  O. Hagolle,et al.  LAI, fAPAR and fCover CYCLOPES global products derived from VEGETATION: Part 1: Principles of the algorithm , 2007 .